Self-Learning System for Debtor Selection and Collector Action Optimization

Credit-line based purchasing power for medium and large-scale businesses may be a used strategy in allowing customers to purchase at ease. However, collecting the money lended in the form of credit is also very crucial as it amounts to a huge sum of money which can be otherwise spent on improving the businesses and reducing the operating expenses. Applying computing systems for the management of such collections may benefit the operation of the businesses

Specific embodiments of the invention will now be described in detail with reference to the accompanying figures. In the following detailed description of the embodiments of the invention, numerous specific details are set forth in order to provide a more thorough understanding of one or more embodiments of the invention. However, it will be apparent to one of ordinary skill in the art that one or more embodiments of the invention may be practiced without these specific details. In other instances, well-known features have not been described in detail to avoid unnecessarily complicating the description.

In the following description of the figures, any component described with regard to a figure, in various embodiments of the invention, may be equivalent to one or more like-named components described with regard to any other figure. For brevity, descriptions of these components will not be repeated with regard to each figure. Thus, each and every embodiment of the components of each figure is incorporated by reference and assumed to be optionally present within every other figure having one or more like-named components. Additionally, in accordance with various embodiments of the invention, any description of the components of a figure is to be interpreted as an optional embodiment, which may be implemented in addition to, in conjunction with, or in place of the embodiments described with regard to a corresponding like-named component in any other figure.

Throughout this application, elements of figures may be labeled as A to N. As used herein, the aforementioned labeling means that the element may include any number of items, and does not require that the element include the same number of elements as any other item labeled as A to N. For example, a data structure may include a first element labeled as A and a second element labeled as N. This labeling convention means that the data structure may include any number of the elements. A second data structure, also labeled as A to N, may also include any number of elements. The number of elements of the first data structure, and the number of elements of the second data structure, may be the same or different.

Throughout the application, ordinal numbers (e.g., first, second, third, etc.) may be used as an adjective for an element (i.e., any noun in the application). The use of ordinal numbers is not to imply or create any particular ordering of the elements nor to limit any element to being only a single element unless expressly disclosed, such as by the use of the terms “before”, “after”, “single”, and other such terminology. Rather, the use of ordinal numbers is to distinguish between the elements. By way of an example, a first element is distinct from a second element, and the first element may encompass more than one element and succeed (or precede) the second element in an ordering of elements.

As used herein, the phrase operatively connected, or operative connection, means that there exists between elements/components/devices a direct or indirect connection that allows the elements to interact with one another in some way. For example, the phrase ‘operatively connected’ may refer to any direct connection (e.g., wired directly between two devices or components) or indirect connection (e.g., wired and/or wireless connections between any number of devices or components connecting the operatively connected devices). Thus, any path through which information may travel may be considered an operative connection.

In general, embodiments disclosed herein include methods and systems for managing collection of assets. Specifically, embodiments of the invention include utilizing an action optimization manager that obtains information from debtors (e.g., entities that owe lent assets to one or more collectors), and apply a debtor scoring model and one or more profile analyses on a set of debtors to generate, for each collector, a debtor portfolio that specifies an order of actions to be taken for communicating with the debtors to collect the borrowed assets. The debtor portfolio for a collector may specify an ordering of the debtors that the corresponding collector is to communicate for obtaining such borrowed assets.

In one or more embodiments, the debtor portfolio is generated by applying action-reward analysis on the set of debtors using state spaces that each specify features of the debtors, optimizing state-action values for each of the set of debtors, and ordering the debtors based on the corresponding state-action values and based on each collector. In this manner, each collector obtains a corresponding debtor portfolio, and each debtor portfolio may specify an ordering tailored to the corresponding collector.

The following describes various embodiments of the invention.

shows a system in accordance with one or more embodiments of the invention. The system () includes any number of debtor devices (), a network (), a collection system (), and one or more data sources (). The system () may include additional, fewer, and/or different components without departing from the scope of the invention. Each component may be operably connected to any of the other component via any combination of wired and/or wireless connections. Each component illustrated inis discussed below.

In one or more embodiments of the invention, the collection system () may provide services to users operating the debtor devices (). The services may be computer-implemented services such as the transfer of assets as loans. For example, one or more of the collection devices () in the collection system () may initiate agreements with one or more debtor devices (,) to loan assets to the debtor devices (,).

At any time after lending the assets to the debtor devices (), the collection devices () may desire collecting on such assets (and/or any interest accrued from the loan). For example, one or more of the debtor devices (,) may be past due on any payment for borrowed assets of one or more of the collection devices (). For example, a first collection device may have lent assets (e.g., money) to three debtors, each operating on a computing device (e.g., a debtor device). Each debtor may be past due on their respective loan payment. There may be an optimal time and order in which the first collection device may desire to contact the three debtors to maximize the amount of return (e.g., expected or total payment) from the three debtors. As such, it may be beneficial to analyze information about the debtor to calculate such optimal time, order, or any other variable.

To perform such calculation, the collection system () includes an action optimization manager () that processes obtained information to calculate, initiate, and/or otherwise deploy, a set of collection actions to be performed by each of the collection devices (). The action optimization manager () may include functionality for other actions without departing from the invention.

For example, the action optimization manager () may include functionality to obtain debtor information from the debtor devices (), such as computer information (e.g., operating system, a user associated with the debtor device, primary methods of communication for the user, etc.). The debtor information may also be obtained from any number of data sources (), external to and separate from the debtor devices (). Each data source may be a third party entity that stores or otherwise includes information associated with the debtor devices (,) and/or any corresponding debtors.

The action optimization manager () may further include functionality for performing data processing on the obtained debtor information to generate debtor portfolios, each including a set and ordering of collection actions, providing the debtor portfolios to corresponding collection devices (), and initiating implementation of the debtor portfolios. The action optimization manager () may perform such data processing in accordance with, for example,. The action optimization agent () may perform other methods to perform the aforementioned data processing without departing from the invention.

In one or more embodiments, the action optimization manager () is implemented as one or more computing devices (e.g.,,). A computing device may be, for example, a mobile phone, a tablet computer, a laptop computer, a desktop computer, a server, a sale terminal, a distributed computing system, or a cloud resource such as a transaction management unit. The computing device may include one or more processors, memory (e.g., RAM), and persistent storage (e.g., disk drives, SSDs, etc.). The computing device may include instructions, stored on the persistent storage, that when executed by the processor(s) of the computing device cause the computing device to perform the functionality of the action optimization manager () (and/or any components illustrated within) described throughout this present disclosure.

Alternatively, in one or more embodiments of the invention, the action optimization manager () is implemented as a logical device. A logical device may utilize the computing resources of any number of computing devices to provide the functionality of the action optimization manager () described throughout this present disclosure including, for example, in. For additional details regarding the action optimization manager (), refer to.

In one or more embodiments of the invention, the collection system () (and/or any components illustrated within) may be implemented as one or more computing devices (e.g.,,). A computing device may be, for example, a mobile phone, a tablet computer, a laptop computer, a desktop computer, a server, a sale terminal, a distributed computing system, or a cloud resource such as a transaction management unit. The computing device may include one or more processors, memory (e.g., RAM), and persistent storage (e.g., disk drives, SSDs, etc.). The computing device may include instructions, stored on the persistent storage, that when executed by the processor(s) of the computing device cause the computing device to perform the functionality of the collection system () (and/or any components illustrated within) described throughout this present disclosure.

Alternatively, in one or more embodiments of the invention, the collection system () (and/or any components illustrated within) may be implemented as logical devices. A logical device may utilize the computing resources of any number of computing devices to provide the functionality of the collection system () (and/or any components illustrated within) described throughout this present disclosure.

In one or more embodiments of the invention, the above-mentioned system () components may operatively connect to one another through a network () (e.g., a local area network (LAN), a wide area network (WAN), a mobile network, a wireless LAN (WLAN), etc.). In one or more embodiments, the network () may be implemented using any combination of wired and/or wireless connections. The network () may encompass various interconnected, network-enabled subcomponents (not shown) (e.g., switches, routers, gateways, etc.) that may facilitate communications between the above-mentioned system () components.

In one or more embodiments of the invention, the network-enabled subcomponents may be capable of: (i) performing one or more communication schemes (e.g., Internet protocol communications, Ethernet communications, communications via any security protocols, etc.); (ii) being configured by the computing devices in the network (); and (iii) limiting communication(s) on a granular level (e.g., on a per-port level, on a per-sending device level, etc.).

shows a diagram of an action optimization manager () in accordance with one or more embodiments. In one or more embodiments, the action optimization manager () includes an optimization agent (), a debtor scoring model (), and a collector portfolio manager (). The action optimization manager () may include additional, fewer, and/or different components without departing from the invention.

In one or more embodiments, the optimization agent () includes functionality for obtaining debtor information and applying at least a portion of the debtor information to the debtor scoring model () to obtain processed information (e.g., in accordance with) and implementing collection actions based on the processed information. The processed information may include, for example, debtor portfolios (,). The processed information may be generated in accordance with, for example, the method shown in.

The collector portfolio manager () may include functionality for distributing the debtor portfolios (,) to the collection devices (,) as discussed above. An example of the generation and distribution of the debtor portfolios (,) may be found in, for example,.

In one or more embodiments, the optimization agent () and the collector portfolio manager () may each be implemented as a software component (e.g., applications of a computing device). In other embodiments, the optimization agent () and the collector portfolio manager () may each be implemented as portions of a non-transitory computer readable medium that includes computer readable program code, which when executed by a computer processor of the action optimization manager enables the computer processor to perform a method for data processing as illustrated, for example, in, discussed below.

shows a flowchart of a method of implementing collection actions using debtor portfolios in accordance with one or more embodiments of the invention. The method shown inmay be performed by, for example, an action optimization manager (e.g.,,). Other components of the system inmay perform all, or a portion, of the method ofwithout departing from the invention.

Whileis illustrated as a series of steps, any of the steps may be omitted, performed in a different order, additional steps may be included, and/or any or all of the steps may be performed in a parallel and/or partially overlapping manner without departing from the invention.

Turning to, in step, debtor information is obtained from one or more data sources. In one or more embodiments, the debtor information is obtained from the debtor devices. Debtor information may be further obtained from data sources that store information associated with the debtors and/or the debtor devices.

In step, a set of state spaces are generated based on debtor attributes obtained from the debtor information. In one or more embodiments, the state spaces are each a vector that specify one or more debtor attributes associated with a vendor. Examples of debtor attributes, of a debtor, specified in a state space include, but are not limited to: a credit amount, a past due amount, a pay gap, call volumes and duration, other engagements, a point in time (e.g., a day, week, quarter, etc.), and past collector portfolio features. The credit amount may include a total amount provided as debt. The past due amount may be an amount owed to a company that has crossed a due date. The call volumes and duration may specify details of past calls received from collections regarding repayment. The other engagements may specify details of emails and/or other forms of engagement received by debtors from collectors regarding repayment. The past collector portfolio features specify features including a number of accounts, an amount of debt, efforts complexity of cases handled by the collector at a first instance.

In one or more embodiments, the state denoted herein by s_t is a feature vector that describes the debtor, historical contact features, seasonality features, past collector knapsack features. The below table shows a non-exhaustive list of features used for the state space.

In step, an action-reward analysis is applied on the set of debtors using the set of state spaces to generate state-action values for each of the set of debtors. The action-reward may be applied by determining a reward for each of the state spaces and determining an action for each of the set of state spaces based on the rewards. The determining of the rewards and actions may be based on a Markov Decision process (MDP), further discussed below.

In one or more embodiments, the actions are binary and are defined as follows:

The reward rreceived by the agent on instance t depends on the debt to be recovered from the debtor i to be contacted d, the opportunity cost of contacting debtor i instead of other debtors out and the past knapsack cost of the collector j, m,

where Q denotes the profit of the items that were grouped under the same knapsack j as the current debtor item i at instance t−1. The constants k_,k_and k_are determined using domain knowledge and can be tuned to provide weightage to the different parameters in the reward function. The reward is modelled as a negative function of the debt to be recovered, the cost of picking the debtor over other debtors and the cost incurred due to placement of this debtor in a debtor portfolio, using which the reinforcement learning agent will use as penalty and learn to minimize these costs

The above MDP is used to learn the state-action value function Q(s,a). The action value function Q(s,a) denotes the expected cumulative reward that can be obtained by contacting the debtor while at state s and taking action a.

This action value function is maximized to obtain optimal actions and optimal policy π* as follows,

In one or more embodiments, the Q(s,a) function is used for the debtor profiles as the value/profit of items (debtor profiles) and the output of the debtor scoring model, w for each debtor profile as the weight of the items to be packed into a number of knapsacks (debtor portfolios) such that the action value function is maximized leading to better debt recovery. Another key feature to note here: the debt selection and the portfolio optimization subtasks are designed interdependently, thereby creating a holistic system that learns from the experiences of each other.

In step, a profile analysis is applied on each debtor on a collector-basis to generate debtor portfolios, each corresponding to a debtor and a collection device (e.g., a collector).

In one or more embodiments, The task of assigning the N debtors to M collector portfolios, where every collector j that has a capacity cis modelled as a multi-knapsack problem. The debtor profiles are modelled as the items to be packed into the knapsack with profit and weight values obtained as the Q value from 5.1.1 and the debtor scores w from the debtor scoring model respectively.

The N debtors are ranked based on the action-value function Q(s,a) and their priority score w provided by the debtor scoring model; these N debtors need to be packed into the portfolios of M collectors such that, the debt recovered is maximized and the capacity constraints of the collector portfolio are not violated. Thus, it's modelled as a stochastic multi-knapsack problem where N items of some expected value Q(s,a) and weight wfor item i are to be packed into M knapsacks of capacity c, such that,

The stochastic multi-knapsack problem is formulated as a MDP and solved using reinforcement learning. The reinforcement learning agent prescribes the optimal packing scheme for the N debtor profiles as M disjoint subsets representing the collector portfolio. The state, action, reward formulations are described as follows,

State Space: The state denoted by s_τ{circumflex over ( )} is a feature vector that describes the current knapsack features, like the current capacity, profit to item ratio, the weight to item ratio and the profit to weight ratio. The below table shows a non-exhaustive list of features used for the state space,

In the above definitions ‘debtor profiles’ are referred to as ‘items’ and the ‘collector portfolios’ (also referred to as debtor portfolios) are referred to as knapsacks, where we have N debtor profiles to be allocated to M collector portfolios where each portfolio j has a capacity c_j. The different features in s_τ are defined as,